Data Science Using Oracle Data Miner and Oracle R Enterprise: Transform Your Business Systems Into an Analytical Powerhouse

ISBN-13: 9781484226131 / Angielski / Miękka / 2016 / 289 str.

Sibanjan Das

Opis

Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables.You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know various options available in the ODM workflow for driving automation. Also, you'll get an understanding of various ways to integrate ODM packages, ORE, and native R packages using PLSQL for automating the processes.Data Science Automation Using Oracle Data Miner and Oracle R Enterprise starts with an introduction to business analytics, covering why automation is necessary and the level of complexity in automation at each analytic stage. Then, it focuses on how predictive analytics can be automated by using Oracle Data Miner and Oracle R Enterprise. Also, it explains when and why ODM and ORE are to be used together for automation.The subsequent chapters detail various statistical processes used for predictive analytics such as calculating attribute importance, clustering methods, regression analysis, classification techniques, ensemble models, and neural networks. In these chapters you will also get to understand the automation processes for each of these statistical processes using ODM and ORE along with their application in a real-life business use case.What you'll learn

• Discover the functionality of Oracle Data Miner and Oracle R Enterprise
• Gain methods to perform in-database predictive analytics
• Use Oracle's SQL and PLSQL APIs for building analytical solutions
• Acquire knowledge of common and widely-used business statistical analysis techniques

Who this book is forIT executives, BI architects, Oracle architects and developers, R users and statisticians.

Spis treści

Introduction

Chapter 1 : Getting Started with Oracle Advanced Analytics

Overview of Data Science and CRISP-DM Methodology

Overview of machine learning and its application in industries

Getting started with Oracle Advanced Analytics- Oracle Data Miner and Oracle R Enterprise

Analytical SQL and PL/SQL functions

Summary

Chapter 2 : Installation and Hello World

Oracle Data Miner Installation

Sample Hello World Oracle Data Miner workflow

Oracle Data Miner components for SQL Developer GUI

Oracle R Enterprise Installation

Sample Hello World program using Oracle R

Summary

Chapter 3: Clustering Methods

Approaches for cluster analysis

K-means algorithm fundamentals

K-means algorithm in Oracle Advanced Analytics

Metrics for evaluating clustering algorithms

Create clusters using Oracle SQL and PLSQL API's

Create clusters using Oracle R Enterprise

Create clusters using Oracle SQL Developer GUI

Case Study - Customer Segmentation

Summary

Chapter 4: Association Rules

Introduction to association rules

Terminologies associated with association rules

Apriori algorithm fundamentals

Identify interesting rules

Association rules using Oracle SQL and PLSQL API's

Association rules using Oracle R Enterprise

Association rules using Oracle SQL Developer GUI

Case Study - Market Basket Analysis

Summary

Chapter 5: Regression Analysis

Understanding Relationships

Introduction to Regression Analysis

OLS Regression fundamentals

OLS Regression using Oracle Advanced AnalyticsGLM and Ridge Regression Overview

GLM Regression using Oracle SQL and PLSQL API's

GLM Regression using Oracle R Enterprise

GLM Regression using Oracle SQL Developer GUI

Case Study - Sales Forecast

Summary

Chapter 6: Classification Techniques

Overview of classification techniques

Logistics Regression fundamentals

Decision Tree fundamentals

SVM fundamentals

Naïve Bayes fundamentals

Classification using Oracle Advanced Analytics

Classification using Oracle SQL and PLSQL API's

Classification using Oracle R Enterprise

Classification using Oracle SQL Developer GUI

Case Study - Customer Churn Prediction

Summary

Chapter 7: Advanced Topics

Overview of Neural Networks

Neural Network using Oracle Advanced Analytics

Overview of Anomaly detection

Anomaly detection using Oracle Advanced Analytics

Overview of Random Forest

Random Forest using Oracle Advanced Analytics

Overview of Predictive Queries

Predictive Queries using Oracle Advanced Analytics

Overview of Product Recommendation Engine

Product Recommendation engine using Oracle Advanced Analytics

Summary

Chapter 8: Solution Deployment

Oracle Data Miner Import and Export functionality

Introduction to PMML

Generating PMML from Oracle Advanced Analytics models

O autorze

Sibanjan is a Sr Analyst for Business Intelligence and Data Science evangelist. He has a strong consulting experience on Business Systems and Data Analytics. As a highly empowered consultant offering around 7 yrs of cross functional experience in the industry, he has helped several organizations to improve, automate and operationalize analytics for their business processes. He comes with a background of implementing business processes using Oracle ERP systems and predictive analytics solutions using Oracle Data Miner and Oracle R Enterprise. He is a Master of Business Analytics from Singapore Management University and holds several certification credentials such as OCA, OCP, ITIL V3, CSCMS and Six Sigma Green belt.

Blurb

Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables.

You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know various options available in the ODM workflow for driving automation. Also, you'll get an understanding of various ways to integrate ODM packages, ORE, and native R packages using PLSQL for automating the processes.